Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation

TL;DR

This paper proposes a reasoning-based SLT framework using latent thoughts and plan-then-ground decoding, improving coherence and faithfulness in gloss-free sign language translation.

Contribution

Introduces a novel reasoning-driven SLT model with latent thoughts and a plan-then-ground decoding approach, plus a new large-scale gloss-free dataset.

Findings

Consistent performance improvements over existing methods.

Enhanced coherence and faithfulness in translation.

New dataset with realistic context dependencies.

Abstract

Many SLT systems quietly assume that brief chunks of signing map directly to spoken-language words. That assumption breaks down because signers often create meaning on the fly using context, space, and movement. We revisit SLT and argue that it is mainly a cross-modal reasoning task, not just a straightforward video-to-text conversion. We thus introduce a reasoning-driven SLT framework that uses an ordered sequence of latent thoughts as an explicit middle layer between the video and the generated text. These latent thoughts gradually extract and organize meaning over time. On top of this, we use a plan-then-ground decoding method: the model first decides what it wants to say, and then looks back at the video to find the evidence. This separation improves coherence and faithfulness. We also built and released a new large-scale gloss-free SLT dataset with stronger context dependencies and more realistic meanings. Experiments across several benchmarks show consistent gains over existing gloss-free methods. Our code and data are available at https://github.com/fletcherjiang/SignThought.